# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Adam optimizer with weight decay that exactly matches the original BERT."""

import re

from absl import logging
import tensorflow as tf


class AdamWeightDecay(tf.keras.optimizers.Adam):
    """Adam enables L2 weight decay and clip_by_global_norm on gradients.

  [Warning!]: Keras optimizer supports gradient clipping and has an AdamW
  implementation. Please consider evaluating the choice in Keras package.

  Just adding the square of the weights to the loss function is *not* the
  correct way of using L2 regularization/weight decay with Adam, since that will
  interact with the m and v parameters in strange ways.

  Instead we want to decay the weights in a manner that doesn't interact with
  the m/v parameters. This is equivalent to adding the square of the weights to
  the loss with plain (non-momentum) SGD.
  """

    def __init__(self,
                 learning_rate=0.001,
                 beta_1=0.9,
                 beta_2=0.999,
                 epsilon=1e-7,
                 amsgrad=False,
                 weight_decay_rate=0.0,
                 include_in_weight_decay=None,
                 exclude_from_weight_decay=None,
                 gradient_clip_norm=1.0,
                 name='AdamWeightDecay',
                 **kwargs):
        super(AdamWeightDecay, self).__init__(learning_rate, beta_1, beta_2,
                                              epsilon, amsgrad, name, **kwargs)
        self.weight_decay_rate = weight_decay_rate
        self.gradient_clip_norm = gradient_clip_norm
        self._include_in_weight_decay = include_in_weight_decay
        self._exclude_from_weight_decay = exclude_from_weight_decay
        logging.info('AdamWeightDecay gradient_clip_norm=%f',
                     gradient_clip_norm)

    def _prepare_local(self, var_device, var_dtype, apply_state):
        super(AdamWeightDecay, self)._prepare_local(
            var_device,
            var_dtype,  # pytype: disable=attribute-error  # typed-keras
            apply_state)
        apply_state[(var_device,
                     var_dtype)]['weight_decay_rate'] = tf.constant(
                         self.weight_decay_rate, name='adam_weight_decay_rate')

    def _decay_weights_op(self, var, learning_rate, apply_state):
        do_decay = self._do_use_weight_decay(var.name)
        if do_decay:
            return var.assign_sub(
                learning_rate * var *
                apply_state[(var.device,
                             var.dtype.base_dtype)]['weight_decay_rate'],
                use_locking=self._use_locking)
        return tf.no_op()

    def apply_gradients(self,
                        grads_and_vars,
                        name=None,
                        experimental_aggregate_gradients=True):
        grads, tvars = list(zip(*grads_and_vars))
        if experimental_aggregate_gradients and self.gradient_clip_norm > 0.0:
            # when experimental_aggregate_gradients = False, apply_gradients() no
            # longer implicitly allreduce gradients, users manually allreduce gradient
            # and passed the allreduced grads_and_vars. For now, the
            # clip_by_global_norm will be moved to before the explicit allreduce to
            # keep the math the same as TF 1 and pre TF 2.2 implementation.
            (grads,
             _) = tf.clip_by_global_norm(grads,
                                         clip_norm=self.gradient_clip_norm)
        return super(AdamWeightDecay, self).apply_gradients(
            zip(grads, tvars),
            name=name,
            experimental_aggregate_gradients=experimental_aggregate_gradients)

    def _get_lr(self, var_device, var_dtype, apply_state):
        """Retrieves the learning rate with the given state."""
        if apply_state is None:
            return self._decayed_lr_t[var_dtype], {}

        apply_state = apply_state or {}
        coefficients = apply_state.get((var_device, var_dtype))
        if coefficients is None:
            coefficients = self._fallback_apply_state(var_device, var_dtype)
            apply_state[(var_device, var_dtype)] = coefficients

        return coefficients['lr_t'], dict(apply_state=apply_state)

    def _resource_apply_dense(self, grad, var, apply_state=None):
        lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype,
                                    apply_state)
        decay = self._decay_weights_op(var, lr_t, apply_state)
        with tf.control_dependencies([decay]):
            return super(AdamWeightDecay,
                         self)._resource_apply_dense(grad, var, **kwargs)  # pytype: disable=attribute-error  # typed-keras

    def _resource_apply_sparse(self, grad, var, indices, apply_state=None):
        lr_t, kwargs = self._get_lr(var.device, var.dtype.base_dtype,
                                    apply_state)
        decay = self._decay_weights_op(var, lr_t, apply_state)
        with tf.control_dependencies([decay]):
            return super(AdamWeightDecay,
                         self)._resource_apply_sparse(grad, var, indices,
                                                      **kwargs)  # pytype: disable=attribute-error  # typed-keras

    def get_config(self):
        config = super(AdamWeightDecay, self).get_config()
        config.update({
            'weight_decay_rate': self.weight_decay_rate,
        })
        return config

    def _do_use_weight_decay(self, param_name):
        """Whether to use L2 weight decay for `param_name`."""
        if self.weight_decay_rate == 0:
            return False

        if self._include_in_weight_decay:
            for r in self._include_in_weight_decay:
                if re.search(r, param_name) is not None:
                    return True

        if self._exclude_from_weight_decay:
            for r in self._exclude_from_weight_decay:
                if re.search(r, param_name) is not None:
                    return False
        return True
